Sliding Window Based Micro-expression Spotting: A Benchmark

Tran, Thuong-Khanh; Hong, Xiaopeng; Zhao, Guoying

doi:10.1007/978-3-319-70353-4_46

Cited by 15 publications

(6 citation statements)

References 38 publications

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“…It is the most commonly used method for result comparison for ME spotting. Based on [7] and [18], the configuration of LBP-χ 2 is set as follows: the entire face region is divided into 36 blocks. The overlap rates between blocks on axis X and Y are are 0.2 and 0.3 respectively.…”

Section: Lbp-χ 2 -Distance Methodsmentioning

confidence: 99%

Spotting Micro-Expressions on Long Videos Sequences

Soladié

Séguier

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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This paper presents two methods for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) and local binary pattern (LBP) on two most recent databases, i.e. SAMM and CAS(ME) 2 . First we propose LTP-ML method as the baseline results for the challenge and then we compare the results with the LBP-χ 2distance method. The LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The LBP-χ 2 -distance method is to compare the feature difference by calculating χ 2 distance of LBP in a time window, the facial movements are then detected with a threshold. The performance is evaluated by Leave-One-Subject-Out cross validation. The overlap frames are used to determine the True Positives and the metric F1-score is used to compare the spotting performance of the databases. The F1-score of LTP-ML result for SAMM and CAS(ME) 2 are 0.0316 and 0.0179, respectively. The results show our proposed LTP-ML method outperformed LBP-χ 2 -distance method in terms of F1-score on both databases.

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Section: Lbp-χ 2 -Distance Methodsmentioning

confidence: 99%

Spotting Micro-Expressions on Long Videos Sequences

Soladié

Séguier

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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“…The study in [23] is the first study utilizing machine learning based on deformable features and the Adaboost classifier to detect ME samples. Tran et al [19] proposed using a multi-scale sliding window based on spatial-temporal feature for ME spotting. In [26], Zhang et al proposed using a Convolutional Neural Network (CNN) to detect the apex frame in two main steps: (1) constructing CNN networks to predict apex frames and neutral frames; (2) introducing a feature engineering technique to merge nearby detected samples.…”

Section: Related Workmentioning

confidence: 99%

DynGeoNet: Fusion Network for Micro-expression Spotting

Tran

Vo²,

Zhao

2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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Micro-expressions (MEs) are brief and involuntary facial expressions when people hide their true feelings or conceal their emotions. Based on psychology research, MEs play an important role in understanding genuine emotions, which leads to many potential applications. However, the ME analysis system can still not work well in the real environment because of the challenging performance of ME spotting, which is to spot the images with micro-expressions from long video sequences. To improve the performance of ME spotting, we focus on hybrid feature engineering, which aims to create a robust feature for discriminating tiny movements. The proposed framework consists of two main modules: (1) the feature engineering extracts both geometric features and appearance features based on dynamic image; (2) the new deep neural network inputs the handcrafted feature for the late fusion and ME samples classification. Our experimental results from three baseline datasets demonstrate the promising results.

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“…Recently, Tran et al (2017) proposed a micro-expression spotting benchmark (MESB) to standardize the performance evaluation of the spotting task. Using a sliding window based multi-scale evaluation and a series of protocols, they recognize the need for a fairer and more comprehensive method of assessment.…”

Section: Spotting Of Facial Micro-expressionsmentioning

confidence: 99%

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

et al. 2018

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Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was largely manual. Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.

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Sliding Window Based Micro-expression Spotting: A Benchmark

Cited by 15 publications

References 38 publications

Spotting Micro-Expressions on Long Videos Sequences

Spotting Micro-Expressions on Long Videos Sequences

DynGeoNet: Fusion Network for Micro-expression Spotting

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

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